Scientia Agricultura Sinica ›› 2012, Vol. 45 ›› Issue (4): 706-713.doi: 10.3864/j.issn.0578-1752.2012.04.011

• HORTICULTURE • Previous Articles     Next Articles

Fast Determination of Nitrogen Status in Pear Leaf Using Mid-Infrared Photoacoustic Spectroscopy

 ZHAO  Hua-Bing, LI  Yan-Li, XIE  Kai, SONG  Xiao-Hui, DONG  Cai-Xia, XU  Yang-Chun   

  1. 1.南京农业大学资源与环境科学学院，南京210095
  • Received:2011-04-14 Online:2012-02-15 Published:2011-12-16

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Abstract: 【Objective】 The aim was to find a fast and convenient method for prediction of the nitrogen status in pear leaves by technique of spectroscopy.【Method】Pear leaves were collected from 200 dominant pear orchards in four pear experimental stations scattered in three provinces (Shandong, Hebei and Liaoning) around the Bohai Gulf which is affiliated to National Pear Industrial Technology System. Fourier transform mid-infrared photoacoustic spectra (FTIR-PAS) of 200 pear leaf samples were recorded, and Partial Least Squares Regression (PLSR) was applied in the modelings of nitrogen content in pear leaf. 【Result】 The effects of the quantities of Principal Components (PCs) in PLSR and the scope of nitrogen concentrations in samples on the quantitative predicting capacity of PLSR were analyzed. It was found that the established model was of highest accuracy when the quantities of major ingredients in the respective experimental stations, which was referred to Yantai, Tai'an, Changli, Yingkou, and the total samples, as a whole were 6, 6, 6, 4 and 8, respectively. It was also found that when predicting the nitrogen contents in samples by the five established models, Root Mean Square Error of Prediction (RMSEP) was ranging from 1.26 g•kg-1 and 2.18 g•kg-1 and the correlation coefficient of Prediction(Rp) was between 0.644 and 0.806.【Conclusion】The results indicated that it is a new fast and convenient technique for predicting the leaf N status of fruit tree by using the Mid-Infrared Photoacoustic Spectroscopy.

Key words: pear, leaf nitrogen content, mid-infrared photoacoustic spectroscopy, partial least squares regression

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